Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06522815
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-22
Brief Title:
Updating Deep Learning Algorithms for OSA Monitoring
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Deep Learning Algorithm Update Using Real Patients for Out-of-hospital Obstructive Sleep Apnea Monitoring
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm
Detailed Description:
Patients undergoing Level 1 polysomnography are equipped with the CART-I PLUS device for collecting polysomnography data alongside concurrent photoplethysmography PPG signals
The collected data is categorized into apnea hypopnea and normal segments based on the polysomnography results Utilizing the PPG and accelerometer ACC signals from the CART-I PLUS metrics such as SaO2 oxygen saturation respiratory rate heart rate HR heart rate variability HRV and body movement are calculated for each segment These metrics along with the PPG and ACC signals are then used to develop a deep learning model that classifies the segments into apnea hypopnea or normal
Participants are divided into training and validation sets The deep learning model is trained on data from the participants in the training set and its performance is evaluated using the validation set
The algorithm is constructed using convolutional neural networks CNN recurrent neural networks RNN attention mechanisms and other advanced techniques recognized for their efficacy in classification tasks specifically for identifying apnea hypopnea and normal segments
The collected data is categorized into apnea hypopnea and normal segments based on the polysomnography results Utilizing the PPG and accelerometer ACC signals from the CART-I PLUS metrics such as SaO2 oxygen saturation respiratory rate heart rate HR heart rate variability HRV and body movement are calculated for each segment These metrics along with the PPG and ACC signals are then used to develop a deep learning model that classifies the segments into apnea hypopnea or normal
Participants are divided into training and validation sets The deep learning model is trained on data from the participants in the training set and its performance is evaluated using the validation set
The algorithm is constructed using convolutional neural networks CNN recurrent neural networks RNN attention mechanisms and other advanced techniques recognized for their efficacy in classification tasks specifically for identifying apnea hypopnea and normal segments
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None